Linking theory with data in spatial ecology and epidemiology

了解生态群落和疾病暴发期间发生的空间模式可以告诉我们许多涉及的潜在过程。了解这些机制反过来可以帮助我们预测和管理这些生物系统。例如，了解哪些环境变量影响病媒或入侵物种的空间传播，或受威胁物种的分布，以及它们在整个景观中移动的能力，将有助于我们了解如何最好地根除、控制或保护它们。数学家建立了许多描述有机体空间动力学的抽象模型，但这些模型很难与数据联系起来。统计学家开发了量化空间模式的工具，但它们可能很难与生物学机制联系起来。 总的来说，我的研究旨在通过开发新的统计技术，扩展现有技术，并将我的结果传播给收集数据的生物学家，将理论模型与生物数据联系起来。在这个研究项目中，我正在开发一种统计技术，可以区分不同类型的环境因素对生物体空间分布模式的影响，如温度、湿度和运动能力的影响。

Understanding the spatial patterns occurring in ecological communities and during outbreaks of disease can tell us a great deal about the underlying processes involved. Understanding these mechanisms can in turn help us to predict and manage these biological systems. For example, knowing which environmental variables affect the spatial spread of disease vectors or invasive species, or the distribution of threatened species, as well as their ability to move across the landscape, will help us understand how best to eradicate, control, or preserve them. Mathematicians have built many abstract models that describe the spatial dynamics of organisms, but these models can be hard to connect with data. Statisticians develop tools for quantifying spatial patterns, but they can be hard to connect with the biological mechanisms. In general, my research aims to connect theoretical models with biological data by developing new statistical techniques, extending existing techniques, and disseminating my results to biologists who collect the data. In this research program, I am developing a statistical technique that can separate the effects of different kinds of environmental factors, such as the effects of temperature, humidity, and movement ability, on spatial distribution patterns of organisms.